Fraudulent Detection Model Using Machine Learning Techniques for Unstructured Supplementary Service Data

The increase in mobile phones accessibility and technological advancement in almost every corner of the world has shaped how banks offer financial service. Such services were extended to low-end customers without a smartphone providing Alternative Banking Channels (ABCs) service, rendering regular financial service same as those on smartphones. One of the services of this ABC’s is Unstructured Supplementary Service Data (USSD), two-way communication between mobile phones and applications, which is used to render financial services all from the bank accounts linked for this USSD service. Fraudsters have taken advantage of innocent customers on this channel to carry out fraudulent activities with high impart of fraudulent there is still not an implemented fraud detection model to detect this fraud activities. This paper is an investigation into fraud detection model using machine learning techniques for Unstructured Supplementary Service Data based on short-term memory. Statistical features were derived by aggregating customers activities using a short window size to improve the model performance on selected machine learning classifiers, employing the best set of features to improve the model performance. Based on the results obtained, the proposed Fraudulent detection model demonstrated that with the appropriate machine learning techniques for USSD, best performance was achieved with Random forest having the best result of 100% across all its performance measure, KNeighbors was second in performance measure having an average of 99% across all its performance measure while Gradient boosting was third in its performance measure, its achieved accuracy is 91.94%, precession is 86%, recall is 100% and f1 score is 92.54%. Result obtained shows two of the selected machine learning random forest and decision tree are best fit for the fraud detection in this model. With the right features derived and an appropriate machine learning algorithm, the proposed model offers the best fraud detection accuracy.

An Unstructured Supplementary Service Data System for Daily Tracking of Patient Samples and Diagnostic Results in a Diagnostic Network in Malawi: System Development and Field Trial

Background Diagnostics in many low- and middle-income countries are conducted through centralized laboratory networks. Samples are collected from patients at remote point-of-care health facilities, and diagnostic tests are performed at centralized laboratories. Sample transportation systems that deliver diagnostic samples and test results are crucial for timely diagnosis and treatment in such diagnostic networks. However, they often lack the timely and accurate data (eg, the quantity and location of samples prepared for collection) required for efficient operation. Objective This study aims to demonstrate the feasibility, adoption, and accuracy of a distributed data collection system that leverages basic mobile phone technology to gather reports on the quantity and location of patient samples and test results prepared for delivery in the diagnostic network of Malawi. Methods We designed a system that leverages unstructured supplementary service data (USSD) technology to enable health workers to submit daily reports describing the quantity of transportation-ready diagnostic samples and test results at specific health care facilities, free of charge with any mobile phone, and aggregate these data for sample transportation administrators. We then conducted a year-long field trial of this system in 51 health facilities serving 3 districts in Malawi. Between July 2019 and July 2020, the participants submitted daily reports containing the number of patient samples or test results designated for viral load, early infant diagnosis, and tuberculosis testing at each facility. We monitored daily participation and compared the submitted USSD reports with program data to assess system feasibility, adoption, and accuracy. Results The participating facilities submitted 37,771 reports over the duration of the field trial. Daily facility participation increased from an average of 50% (26/51) in the first 2 weeks of the trial to approximately 80% (41/51) by the midpoint of the trial and remained at or above 80% (41/51) until the conclusion of the trial. On average, more than 80% of the reports submitted by a facility for a specific type of sample matched the actual number of patient samples collected from that facility by a courier. Conclusions Our findings suggest that a USSD-based system is a feasible, adoptable, and accurate solution to the challenges of untimely, inaccurate, or incomplete data in diagnostic networks. Certain design characteristics of our system, such as the use of USSD, and implementation characteristics, such as the supportive role of the field team, were necessary to ensure high participation and accuracy rates without any explicit financial incentives.

An Unstructured Supplementary Service Data System for Daily Tracking of Patient Samples and Diagnostic Results in a Diagnostic Network in Malawi: System Development and Field Trial (Preprint)

BACKGROUND Diagnostics in many low- and middle-income countries are conducted through centralized laboratory networks. Samples are collected from patients at remote point-of-care health facilities, and diagnostic tests are performed at centralized laboratories. Sample transportation systems that deliver diagnostic samples and test results are crucial for timely diagnosis and treatment in such diagnostic networks. However, they often lack the timely and accurate data (eg, the quantity and location of samples prepared for collection) required for efficient operation. OBJECTIVE This study aims to demonstrate the feasibility, adoption, and accuracy of a distributed data collection system that leverages basic mobile phone technology to gather reports on the quantity and location of patient samples and test results prepared for delivery in the diagnostic network of Malawi. METHODS We designed a system that leverages unstructured supplementary service data (USSD) technology to enable health workers to submit daily reports describing the quantity of transportation-ready diagnostic samples and test results at specific health care facilities, free of charge with any mobile phone, and aggregate these data for sample transportation administrators. We then conducted a year-long field trial of this system in 51 health facilities serving 3 districts in Malawi. Between July 2019 and July 2020, the participants submitted daily reports containing the number of patient samples or test results designated for viral load, early infant diagnosis, and tuberculosis testing at each facility. We monitored daily participation and compared the submitted USSD reports with program data to assess system feasibility, adoption, and accuracy. RESULTS The participating facilities submitted 37,771 reports over the duration of the field trial. Daily facility participation increased from an average of 50% (26/51) in the first 2 weeks of the trial to approximately 80% (41/51) by the midpoint of the trial and remained at or above 80% (41/51) until the conclusion of the trial. On average, more than 80% of the reports submitted by a facility for a specific type of sample matched the actual number of patient samples collected from that facility by a courier. CONCLUSIONS Our findings suggest that a USSD-based system is a feasible, adoptable, and accurate solution to the challenges of untimely, inaccurate, or incomplete data in diagnostic networks. Certain design characteristics of our system, such as the use of USSD, and implementation characteristics, such as the supportive role of the field team, were necessary to ensure high participation and accuracy rates without any explicit financial incentives.

Mobile phone banking: A comparative analysis of e-service quality and customer loyalty of banking applications and Unstructured Supplementary Service Data services

Orientation: Mobile devices are becoming a popular means to access the Internet and conduct e-banking activities. Banks are marketing e-banking solutions to increase their customer base.Research purpose: This study contrasts how the service quality of mobile phone applications and Unstructured Supplementary Service Data banking influence customer loyalty. The study examined e-service quality to validate and determine the nature of the relationship of the proposed model.Motivation for the study: The study was conducted in a mobile banking (MB) setting in South Africa, with a technologically savvy young adult population. To win customer loyalty, banks need to provide innovative solutions to this group of clients.Research approach/design and method: A positivistic paradigm with a quantitative approach and descriptive design was adopted for this study. A convenience sampling method was used to distribute a questionnaire to 300 students, with a response rate of 73%.Main findings: The findings showed that fulfilment, privacy and efficiency were all significantly related to overall service quality and customer loyalty for both banking models. Based on the findings, privacy, fulfilment and efficiency are critical facets to both MB applications and Unstructured Supplementary Service Data banking.Practical/managerial implications: Banks need to target ‘unbanked’ customers to register for a bank account via Unstructured Supplementary Service Data banking or MB applications.Contribution/value-add: However, there is still a lack of knowledge about these services among the population. Therefore, banks need to educate potential and existing customers on these products and services to fully exploit the market segments.

DAYA SAING UMKM MELALUI PERSPEKTIF CORE PRODUCT DAN SUPPLEMENTARY SERVICE

Usaha Mikro Kecil dan Menengah (UMKM) memiliki peran penting bagi perekonomian Indonesia, namun daya saing UMKM masih rendah, salah satu penyebabnya adalah lemahnya kualitas pelayanan. Tujuan penelitian ini adalah untuk mengetahui pengaruh kualitas pelayanan“the flower of service” terhadap loyalitas konsumen. Objek penelitian adalah UMKM CENTER Jawa Tengah. Penentuan sampel dengan metode purpossive sampling, sebanyak 100 responden. Analisis menggunakan Regresi Linier Sederhana. Hasil dari penelitian ini membuktikan dan memberi kesimpulan bahwa: kualitas pelayanan“the flower of service” berpengaruh positif dan signifikan terhadap loyalitas konsumen. UMKM CENTER Jawa Tengah memiliki 8 kelopak “the flower of service” dengan nilai score ≥3,41, artinya UMKM memiliki daya saing optimal dan layanan jasa yang baik, nilai rincian sebagai berikut : information (4,17), consultation (4,29), ordertaking (4,08), hospitality (4,2), safekeeping (4,18), exception (4,21), billing (4,04) dan payment (4,09). Dari kelopak indikator tersebut, nilai score tertinggi adalah 4,29 yaitu consultation, sedangkan nilai score terendah adalah billing. <br /> <br /> Kata Kunci: UMKM, kualitas pelayanan (the flower of service) dan loyalitas konsumen.

Pengaruh The Flower Of Service dan Physical Evidence terhadap Loyalitas Konsumen pada Starbucks Coffe

The concept of Flower of Service was first introduced by Christopher Lovelock, a British service marketing expert. According to this concept, value is constructed by core products and supplementary services. There are two identified supplementary service, first is a supplementary service that acts as deliverer of service or aid in the usage of core products. And second is a supplementary service that strengthens and adds value to the customers. Mean while, physical evidence is recognized as one of the many factors that influence the customers to buy or use the offered services, and customers’ loyalty is a chance for the buyers to become a loyal customers and /or clients. This research is aimed to (1) find out about the influence of flower of service on customers’ loyalty, (2) to examine the influence of physical evidence on customer loyalty, and (3) to find out the influence of flower of service and physical evidence on customer loyalty. The research show that the multiple linear regression equation Y=3,786+0,482 X1+0,348 X2 which means that if flower of service is represented with 1 then the level of customers’ loyalty will increase for an amount of 0,482 and if physical evidence is 1 then the level of customers’ loyalty will increase for an amount of 0,348. The result of coefficient correlation analysis show that r = 0,642 which means that there is a significant relationships between flower of service and physical evidence on customers’ loyalty. The analysis for the coefficient determination proves that flower of service contributes 41,2% in creating customers’ loyalty and the rest of 58,8% was determined by other factors.